Single tool 'get_profile' has a verb-based name (good), but the implementation reveals critical gaps. Description is adequate (67 chars, within baseline range), but input schema is minimal, only 'linkedin_url' with basic string type and no format/pattern constraints. No output schema is documented; the tool returns raw JSON.dumps() of API response without structure definition. Error handling is minimal (returns generic 'Unable to fetch LinkedIn profile data.' on any exception). No parameter validation, no guidance on URL format, no documentation of response fields. The tool lacks idempotency guarantees and does not strip irrelevant API metadata. Overall, this is a bare-minimum implementation that would confuse LLMs about expected output structure and error recovery.

• Document the output schema explicitly. Define which fields are returned (e.g., name, headline, location, skills array), their types, and which are always present vs. optional. Example: {"name": string, "headline": string, "location": string, "skills": [string], "profile_url": string}.
• Add format constraint to 'linkedin_url' parameter. Specify that it must be a valid LinkedIn profile URL (e.g., 'https://www.linkedin.com/in/[username]/' or 'https://linkedin.com/in/[username]'). Add a regex pattern or explicit format description.
• Implement categorized error handling. Return distinct error messages: 'Invalid LinkedIn URL format' (user-fixable), 'LinkedIn API timeout, retry in 30s' (retryable), 'API rate limit exceeded, try again in 1 hour' (retryable with backoff), 'Authentication failed, check RAPIDAPI_KEY' (fatal).
• Strip and reshape API response. Return only essential fields (name, headline, location, skills, profile_url) as a structured object. Remove internal API metadata, timestamps, and unused fields to reduce token cost.
• Add idempotency and caching guidance. Document that repeated calls with the same URL return the same data (read-only operation). Consider adding a 'cache_ttl' parameter or note that results are fresh from the API.